Design and multi-objective optimization of a CO₂ capture plant using deep eutectic solvents

The United Nation's 2030 Agenda for Sustainable Development has called for taking of urgent action to combat climate change and its impacts, where CO₂ capture processes have gained relevance for achieving emission objectives. Traditionally, monoethanolamine (MEA) is used in post-combustion capture (PCC) as a solvent because of its high absorption capacity. However, the use of amines is related to several environmental problems. In this work a novel post-combustion carbon capture plant is designed in Aspen Plus, under a sustainable design scheme considering environmental and economic indexes, using novel green liquids: deep eutectic solvent (DES) choline chloride/urea (1:2). Four case studies are considered for the most used fossil fuels in the power plant: coal, natural gas, associated gas, and biogas. This work represents a base case for multi-objective optimization of the PCC process coupled with a power plant for combustion. An optimization problem was defined considering the following objective functions: minimization of the environmental impact (EcoIndicator 99), minimization of the total solvent recovery energy (TSRE) and greenhouse gas emissions (GHGE); the economic objectives were set to minimize the total annual cost of the plant (TAC) and maximize return over investment (ROI). A metaheuristic optimization method, differential evolution with tabu list was chosen, and Pareto fronts for the objective functions were obtained for all case studies. It was found that the PCC process for coal-based power generation exhibits the best overall performance among all evaluated fuels. A comparison with a similarly optimized MEA-based carbon capture process shows that the use of DES leads to a 171.8% reduction in environmental impact when treating flue gas from a coal-based power process. To showcase the feasibility of the DES as a green solvent, an energy consumption of 6.62 MJ/kgCO₂ is needed, compared with 6.40 MJ/kgCO₂ for the MEA-based process.